We empirically demonstrate that our theoretical analysis explains why our algorithm works. To achieve this, we analyze the shift in latent embedding before and after adaptation, and discuss how the \(\rho \)  parameter influences the prototypical distribution approximation in the latent space. The other hyper-parameter used in our approach, the regularizer \(\lambda \) , was empirically observed to not significantly influence results. In order to visualize these embeddings, we use UMAP [1]} which allows for embedding high-dimensional distributions into \(2D\)  space for \(2D\)  visualization.
